Mathematical Oncology: From methodological studies to clinical applications

Wednesday, June 16 at 04:15am (PDT)
Wednesday, June 16 at 12:15pm (BST)
Wednesday, June 16 08:15pm (KST)

SMB2021 SMB2021 Follow Tuesday (Wednesday) during the "MS12" time block.
Note: this minisymposia has multiple sessions. The second session is MS11-ONCO (click here).

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Saskia Haupt (Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Germany), Vincent Heuveline (Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Germany), Matthias Kloor (Department of Applied Tumor Biology (ATB), Institute of Pathology, University Hospital Heidelberg, Germany)


While the understanding of cancer development has dramatically increased during the last years, key questions with immediate implications for clinical management and prevention strategies remain still unanswered. In the framework of the mini-symposium, we will present current research work in Mathematical Oncology addressing these challenges. Talks range from mathematical analyses and methodological studies to medical and clinical applications with a strong focus on interdisciplinary collaborations and diversity.

Natalia Komarova

(Department of Mathematics, University of California Irvine, Irvine, California, USA)
"CLL and the drug Ibrutinib: modeling and clinical applications"
Chronic Lymphocytic leukemia is the most common leukemia, mostly arising in patients over the age of 50. The disease has been treated with chemo-immunotherapies with varying outcomes, depending on the genetic make-up of the tumor cells. Recently, a promising tyrosine kinase inhibitor, ibrutinib, has been developed, which resulted in successful responses in clinical trials, even for the most aggressive chronic lymphocytic leukemia types. The crucial questions include how long disease control can be maintained in individual patients, when drug resistance is expected to arise, and what can be done to counter it. Computational evolutionary models, based on measured kinetic parameters of patients, allow us to address these questions and to pave the way toward a personalized prognosis.

Johannes G Reiter

(Canary Center for Cancer Early Detection, Department of Radiology, Stanford University, California, USA)
"Minimal intermetastatic heterogeneity"
Genetic intratumoral heterogeneity is a natural consequence of imperfect DNA replication. Any two randomly selected cells, whether normal or cancerous, are therefore genetically different. I will discuss the extent of genetic heterogeneity among untreated cancers with particular regard to its clinical relevance and how it can be exploited to identify metastatic seeding patterns. While genomic heterogeneity within primary tumors is associated with relapse, heterogeneity among treatment‑naïve metastases has not been comprehensively assessed. Within individual patients a large majority of driver gene mutations are common to all metastases. Further analysis revealed that the driver gene mutations that were not shared by all metastases are unlikely to have functional consequences. A mathematical model of tumor evolution and metastasis formation provides an explanation for the observed driver gene homogeneity. Based on a statistical framework for quantifying metastatic phylogenetic diversity in dozens of inferred phylogenies of colorectal cancer patients, distant metastases were typically monophyletic and genetically similar to each other. Lymph node metastases, in contrast, exhibited high levels of inter-lesion diversity. These data indicate that the cells within the primary tumors that give rise to distant metastases evolved through a narrow bottleneck and are generally more homogeneous than the primary tumor and lymph node metastases.

Kamila Naxerova

(Center for Systems Biology, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA)
"On the evolutionary history of metastatic cancer"
The evolutionary history of metastases is still largely unknown. Do metastases arise from distinct clones with special, genetically encoded properties or do they represent random samples of the primary tumor? Does metastatic spread happen early or late in tumor development? Do all metastases arise independently from the primary tumor, or do they give rise to each other? How heterogeneous are metastases? These fundamental questions have profound clinical implications but are difficult to study in human patients because relevant events predate diagnosis by many years. We are developing methods for high-efficiency lineage tracing in human tumor samples and apply these to study the roots of metastatic disease. Here, the joint insights from multiple published and unpublished studies will be presented.

Saskia Haupt

(Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Germany)
"A computational model for investigating the evolution of colonic crypts during Lynch syndrome carcinogenesis"
Introduction Lynch syndrome (LS), the most common inherited colorectal cancer (CRC) syndrome, increases the cancer risk in affected individuals. LS is caused by pathogenic germline variants in one of the DNA mismatch repair (MMR) genes, complete inactivation of which causes numerous mutations in affected cells. As CRC is believed to originate in colonic crypts, understanding the intra-crypt dynamics caused by mutational processes is essential for a complete picture of LS CRC and may have significant implications for cancer prevention. Methods We propose a computational model describing the evolution of colonic crypts during LS carcinogenesis. Extending existing modeling approaches for the non-Lynch scenario, we incorporated MMR deficiency and implemented recent experimental data demonstrating that somatic CTNNB1 mutations are common drivers of LS-associated CRCs if affecting both alleles of the gene. Further, we simulated the effect of different mutations on the entire crypt, distinguishing non-transforming and transforming mutations. Results As an example, we analyzed the spread of mutations in the genes APC and CTNNB1, which are frequently mutated in LS tumors, as well as of MMR deficiency itself. We quantified each mutation's potential for monoclonal conversion and investigated the influence of the cell location and of stem cell dynamics on mutation spread. Conclusion The in silico experiments underline the importance of stem cell dynamics for the overall crypt evolution. Further, simulating different mutational processes is essential in LS since mutations without survival advantages (the MMR deficiency-inducing second hit) play a key role. The effect of other mutations can be simulated with the proposed model. Our results provide first mathematical clues for effective surveillance protocols for LS carriers.

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Virtual conference of the Society for Mathematical Biology, 2021.